MACT Existing UPL for Mercury Using Pooled Variance, Corrected for Temporal Autocorrelation with Intraquarry Variability - 07/28/10, 3:10PM

Runs Kiln ID 1248Kiln ID 1259 Kiln ID 1286 1 18.8827.01 2 15.7728.73 3 20.7128.67 4 22.6127.95 5 19.67 7.66 6 25.90 7.49 7 22.63 7.58 8 21.21 8.12 9 5.0723.93 10 44.9622.61 11 34.3723.06 12 13.4624.21 13 11.5725.25 14 13.9021.47 15 27.43 25.53 16 25.5525.89 17 21.9529.34 18 12.1026.03 19 12.83 7.60 20 9.1824.26 21 8.7423.68 22 17.63 19.77 23 47.3021.97 24 33.53 22.80 25 19.1123.23 26 25.0931.24 27 14.9430.64 28 17.8932.58 29 24.4628.53 30 39.85 31 12.95 32 33 34 35

ni = number runs = 0 31 29

ni-1 30 28

= sum of (ni-1) 268means 21.33 22.65

Mean 20.69

N = Number sources= 9

m= number future runs = 30

t-statistic = quantile t-distribution with df degrees of freedom at .99 confidence level =

Assuming Correlated variables

correlation =correl(data-last,data - first obs)

0.1357461540.54308166 Pooled stdev= 9.62605957 Variance data = 92.6610229 adjustment for temporal correlation= 111.996455 intra quarry variance= 116.012236 adjustment for temporal correlation= 175.953767 total variance 287.950222 *** QUESTION FOR MIKE: DOES TEMPORAL CORRELATION AFFECT THE QUARRY OR ONLY THE KILNS? *** IN OTHER WORDS, WHAT YOU GET FROM THE QUARRY ONE DAY DEPENDS OF WHAT YOU GET THE PREVIOUS DAYS? *** If NOT, THEN ONLY THE MEASUREMENTS HAVE TEMPORAL CORRELATION, IF YES: THEN BOTH THE VARIANCE OF *** DATA AND INTRAQUARRY VARIANCE NEED TO BE MULTIPLIED BY (1+CORR) *** if the data in columns Oto Q are the intraquarry data, we can calculate the temporal correlation there and use it for the adjustment of the intraquarry variance *** defined as sdev^2 *** defined as before variance* (1+corr) *** defined as (RSD*Mean)^2Variance = Variance pooled, intraquarry data adjusted for temporal correlation 287.950222
SQRT(Variance*(1/n+1/m)) 5.98127358

UPL-- source mean and variance pooled data, including intraquarry variability **34.6879487**

57.2351153MACT Existing UPL for Mercury Using Pooled Variance, Corrected for Temporal Autocorrelation with Intraquarry Variability - 07/28/10, 3:10PM

Kiln ID 1302Kiln ID 1315Kiln ID 1364Kiln ID 1435Kiln ID 1484Kiln ID 1589Kiln ID 1650 16.82 16.67 26.00 30.50 30.50 13.22 12.97 12.76 28.27 27.52 27.52 7.64 9.49 9.57 27.59 29.07 29.07 9.53 10.43 10.58 37.25 24.64 24.64 1.71 9.44 9.64 28.78 23.82 23.82 7.66 11.47 11.71 7.72 24.52 24.52 0.32 13.59 13.83 42.98 25.14 25.14 14.12 12.85 12.72 25.28 26.46 26.46 13.70 4.30 4.38 24.67 25.49 25.49 12.72 12.04 12.29 25.39 38.60 38.60 11.24 21.41 20.55 24.24 24.65 24.65 10.56 13.65 13.82 23.34 29.45 29.45 9.82 12.44 12.54 25.04 25.04 25.04 12.92 16.52 16.66 23.65 29.91 29.91 13.70 22.02 22.51 26.83 1.43 1.43 9.46 12.31 12.31 27.19 5.07 5.07 7.66 15.39 15.11 24.25 25.97 25.97 12.36 13.51 13.59 25.28 29.30 29.30 13.98 3.75 3.82 24.83 26.93 26.93 9.40 9.40 9.59 23.88 26.63 26.63 8.68 10.88 11.06 23.69 24.82 24.82 7.60 12.45 12.35 21.74 24.11 24.11 7.62 11.76 11.89 26.04 22.00 22.00 7.63 11.73 11.75 7.28 22.00 22.00 8.20 60.15 61.31 26.36 22.00 22.00 11.27 20.59 19.93 36.12 35.74 35.74 8.93 13.96 14.10 28.96 24.47 24.47 7.64 21.90 21.19 29.82 38.69 38.69 7.60 12.74 11.85 29.05 27.28 27.28 28.41 27.66 26.93 26.93 23.83 23.83 24.11 24.11 27.28 27.28 24.82 24.82 24.47 24.47

30 30 29 35 35 0 28

29 29 28 34 34 27 Emissions (lb/ton feed)15.28 15.26 25.91 25.51 25.51 9.53

#source mean = mean of source means

#because UPL is based on source means

= quantile t-distribution with df degrees of freedom at .99 confidence level = 2.34034229 =tinv((1-.99)*2,df)

0.102131640.084305058-0.187748050.236712450.23671245 0.19500164

Revised 3 quarry, intraquarry variability RSD= 0.52

*** QUESTION FOR MIKE: DOES TEMPORAL CORRELATION AFFECT THE QUARRY OR ONLY THE KILNS? *** IN OTHER WORDS, WHAT YOU GET FROM THE QUARRY ONE DAY DEPENDS OF WHAT YOU GET THE PREVIOUS DAYS? *** If NOT, THEN ONLY THE MEASUREMENTS HAVE TEMPORAL CORRELATION, IF YES: THEN BOTH THE VARIANCE OF *** DATA AND INTRAQUARRY VARIANCE NEED TO BE MULTIPLIED BY (1+CORR) *** if the data in columns Oto Q are the intraquarry data, we can calculate the temporal correlation there and use it for the adjustment of the intraquarry variance *** defined as sdev^2 *** defined as before variance* (1+corr) *** defined as (RSD*Mean)^2#calculated as variance*(1+average(temporal correlation))

lb/MM tons feed
lb/MM tons clinkerKiln ID 1594 **Kiln ID # 1239** **1248 1376** 

13.60 0.0795 0.0265 30.05 0.0233 0.0197 32.88 0.0296 0.0214 32.59 0.0245 0.0326 28.73 0.02 0.0225 34.14 0.0495 0.02 32.33 0.0462 0.0337 23.38 0.0315 0.0365 35.00 0.0264 0.0344 32.91 0.0294 0.0313 31.44 0.0263 0.0312 27.48 0.0242 0.028 28.55 0.0407 0.0317 25.19 0.0309 0.0207 25.63 0.0235 0.0274 10.01 0.0295 0.0263 28.03 0.0673 0.0211 30.27 0.0625 0.0177 25.85 0.0624 0.0318 33.36 0.0899 0.0246 29.10 0.0835 0.0289 26.91 0.122 0.0232 29.70 0.0998 0.0292 30.02 0.0991 0.0302 10.91 0.109 0.036 11.87 0.13 0.0291 14.04 0.104 0.0296 13.49 0.0589 0.0265 15.72 0.0729 0.0257 13.75 0.0856 0.0255

30

29 **Daily Mercury Concentrations (ppm) in Limestone by Quarry**Avg 0.0594 0.0270.043 Stdev 0.023 RSD 0.520592

correlation 0.8052 0.22820.5167 25.23

=tinv((1-.99)*2,df)

0.532073044

*** IN OTHER WORDS, WHAT YOU GET FROM THE QUARRY ONE DAY DEPENDS OF WHAT YOU GET THE PREVIOUS DAYS? *** If NOT, THEN ONLY THE MEASUREMENTS HAVE TEMPORAL CORRELATION, IF YES: THEN BOTH THE VARIANCE OF *** if the data in columns Oto Q are the intraquarry data, we can calculate the temporal correlation there and use it for the adjustment of the intraquarry varianceMACT New UPL for Mercury Using Pooled Variance, Corrected for Temporal Autocorrelation with Intraquarry Variability - 07/28/10, 3:10PM

Runs Kiln ID 1248Kiln ID 1259 Kiln ID 1286 1 0.32 18.8827.01 2 15.53 15.7728.73 3 17.43 20.7128.67 4 16.57 22.6127.95 5 18.30 19.67 7.66 6 17.06 25.90 7.49 7 16.57 22.63 7.58 8 21.00 21.21 8.12 9 16.57 5.0723.93 10 16.57 44.9622.61 11 16.57 34.3723.06 12 16.57 13.4624.21 13 16.57 11.5725.25 14 20.45 13.9021.47 15 16.57 27.43 25.53 16 16.99 25.5525.89 17 16.60 21.9529.34 18 16.57 12.1026.03 19 16.57 12.83 7.60 20 16.57 9.1824.26 21 16.57 8.7423.68 22 16.57 17.63 19.77 23 16.65 47.3021.97 24 19.14 33.53 22.80 25 21.37 19.1123.23 26 16.57 25.0931.24 27 16.57 14.9430.64 28 16.57 17.8932.58 29 16.30 24.4628.53 30 16.58 39.85 31 12.95 32 33 34 35

ni = number runs = 30 31 29

ni-1 29 30 28= sum of (ni-1) 27

means 16.63 21.33 22.65

Mean 9.53

n to be used with 1 source= ni 28 m= number future runs = 30

t-statistic = quantile t-distribution with df degrees of freedom at .99 confidence level =

Assuming Correlated variables

correlation =correl(data-last,data - first obs)

0.20273792 0.1357461540.54308166 Stdev= 3.35459919 Variance data = 11.2533357 adjustment for temporal correlation= 13.4477546 intra quarry variance= 24.6268249 adjustment for temporal correlation= 37.3510826 total variance 50.7988372 *** defined as variance of data + intra quarry variance *** if the data in columns Oto Q are the intraquarry data, we can calculate the temporal correlation there and use it for the adjustment of the intraquarry variance *** defined as sdev^2 *** defined as before variance* (1+corr) *** defined as (RSD*Mean)^2Variance = Variance pooled, intraquarry data adjusted for temporal correlation 50.799
SQRT(Variance*(1/n+1/m)) 1.873

UPL-- source mean and variance pooled data, including intraquarry variability **14.16** 23.37

Recall the formula for the variance is N is the number of sampling units used in the sample: in this case the number of data points 2

### 1 1 sN m       MACT New UPL for Mercury Using Pooled Variance, Corrected for Temporal Autocorrelation with Intraquarry Variability - 07/28/10, 3:10PM

Kiln ID 1302Kiln ID 1315Kiln ID 1364Kiln ID 1435Kiln ID 1484MACT NewKiln 1650Kiln ID 1650 16.82 16.67 26.00 30.50 30.50 13.22 13.22 12.97 12.76 28.27 27.52 27.52 7.64 7.64 9.49 9.57 27.59 29.07 29.07 9.53 9.53 10.43 10.58 37.25 24.64 24.64 1.71 1.71 9.44 9.64 28.78 23.82 23.82 7.66 7.66 11.47 11.71 7.72 24.52 24.52 0.32 0.32 13.59 13.83 42.98 25.14 25.14 14.12 14.12 12.85 12.72 25.28 26.46 26.46 13.70 13.70 4.30 4.38 24.67 25.49 25.49 12.72 12.72 12.04 12.29 25.39 38.60 38.60 11.24 11.24 21.41 20.55 24.24 24.65 24.65 10.56 10.56 13.65 13.82 23.34 29.45 29.45 9.82 9.82 12.44 12.54 25.04 25.04 25.04 12.92 12.92 16.52 16.66 23.65 29.91 29.91 13.70 13.70 22.02 22.51 26.83 1.43 1.43 9.46 9.46 12.31 12.31 27.19 5.07 5.07 7.66 7.66 15.39 15.11 24.25 25.97 25.97 12.36 12.36 13.51 13.59 25.28 29.30 29.30 13.98 13.98 3.75 3.82 24.83 26.93 26.93 9.40 9.40 9.40 9.59 23.88 26.63 26.63 8.68 8.68 10.88 11.06 23.69 24.82 24.82 7.60 7.60 12.45 12.35 21.74 24.11 24.11 7.62 7.62 11.76 11.89 26.04 22.00 22.00 7.63 7.63 11.73 11.75 7.28 22.00 22.00 8.20 8.20 60.15 61.31 26.36 22.00 22.00 11.27 11.27 20.59 19.93 36.12 35.74 35.74 8.93 8.93 13.96 14.10 28.96 24.47 24.47 7.64 7.64 21.90 21.19 29.82 38.69 38.69 7.60 7.60 12.74 11.85 29.05 27.28 27.28 28.41 27.66 26.93 26.93 23.83 23.83 24.11 24.11 27.28 27.28 24.82 24.82 24.47 24.47

30 30 29 35 35 28 28

29 29 28 34 34 27 27 Emissions (lb/ton feed)15.28 15.26 25.91 25.51 25.51 9.53 9.53

#source mean = mean of source means

= quantile t-distribution with df degrees of freedom at .99 confidence level = 2.4726599 =tinv((1-.99)*2,df)

0.102131640.084305058-0.187748050.236712450.236712450.195001640.19500164

Revised 3 quarry, intraquarry variability RSD= 0.52

*** defined as variance of data + intra quarry variance *** if the data in columns Oto Q are the intraquarry data, we can calculate the temporal correlation there and use it for the adjustment of the intraquarry variance *** defined as sdev^2 *** defined as before variance* (1+corr) *** defined as (RSD*Mean)^2lb/MM tons feed

lb/MM tons clinker
N is the number of sampling units used in the sample: in this case the number of data pointsKiln ID 1594 **Kiln ID # 1239** **1248 1376** 

13.60 0.0795 0.0265 30.05 0.0233 0.0197 32.88 0.0296 0.0214 32.59 0.0245 0.0326 28.73 0.02 0.0225 34.14 0.0495 0.02 32.33 0.0462 0.0337 23.38 0.0315 0.0365 35.00 0.0264 0.0344 32.91 0.0294 0.0313 31.44 0.0263 0.0312 27.48 0.0242 0.028 28.55 0.0407 0.0317 25.19 0.0309 0.0207 25.63 0.0235 0.0274 10.01 0.0295 0.0263 28.03 0.0673 0.0211 30.27 0.0625 0.0177 25.85 0.0624 0.0318 33.36 0.0899 0.0246 29.10 0.0835 0.0289 26.91 0.122 0.0232 29.70 0.0998 0.0292 30.02 0.0991 0.0302 10.91 0.109 0.036 11.87 0.13 0.0291 14.04 0.104 0.0296 13.49 0.0589 0.0265 15.72 0.0729 0.0257 13.75 0.0856 0.0255

30

29 **Daily Mercury Concentrations (ppm) in Limestone by Quarry**Avg 0.0594 0.0270.043 Stdev 0.023 RSD 0.520592

correlation 0.8052 0.22820.5167 25.23

=tinv((1-.99)*2,df)

0.532073044

*** if the data in columns Oto Q are the intraquarry data, we can calculate the temporal correlation there and use it for the adjustment of the intraquarry variance